1978-2016年中国农业生态效率时空演变及趋势预测

doi:10.11821/dlxb201811009

摘要/Abstract

摘要：

基于1978-2016年中国各省市面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列分析和空间相关性分析的基础上,构建传统和空间马尔可夫概率转移矩阵,探讨中国农业生态效率的时空动态演变特征,并预测其长期演变的趋势。研究发现：① 中国农业生态效率呈现出在波动中稳定上升的“双峰”分布特征,且波峰高度的差距在缩小,但整体仍处于较低水平,农业生态效率仍存在较大提升空间,东部地区农业生态效率提升较中西部地区更加显著;② 中国农业生态效率整体上向高水平方向转移的趋势显著,但农业生态效率的演变具有维持原有状态的稳定性,且较难实现跨越式转移。地理空间格局在农业生态效率时空演变过程中发挥着重要作用,空间集聚特性显著,农业生态效率较高的省市具有正向的溢出效应,而农业生态效率较低的省市具有负的溢出效应,从而在空间格局上逐渐形成“高高集聚、低低集聚、高辐射低、低抑制高”的“俱乐部收敛”现象;③ 从长期演变的趋势预测来看,多数省市农业生态效率逐渐向上转移为较高水平,并逐渐演变为由低到高渐次递增的格局,在农业生态效率较低的地理背景下,其长期演变的稳定状态表现为偏“单峰”分布,而在农业生态效率较高的地理背景下,其长期演变为较高水平集聚的偏“双峰”分布。最后,分析当前研究需要改进的方向,并提出控制农业污染排放量、地区间农业生态政策联动、加强地区间农业生态合作交流与借鉴等能够有效提升中国农业生态效率及缩小省市间差距。

关键词: 农业生态效率, 时空演变, 趋势预测, 超效率SBM模型, 空间马尔可夫链, 中国

Abstract:

Based on the panel data of 30 provinces in China from 1978 to 2016, the super efficiency SBM model was used to measure the inter-provincial agricultural eco-efficiency in our study. On the basis of time series analysis and spatial correlation analysis, traditional and spatial Markov probability transfer matrices were constructed to explore the spatial and temporal evolution of agricultural eco-efficiency of China, and the long-term trends were also predicted. The result shows that: (1) The agricultural eco-efficiency in China presents a "double-peak" distribution with stable rise in fluctuation, and the gap between peak heights is narrowing, but the overall level is still relatively low. Therefore, there is still room for improvement in agricultural eco-efficiency. Besides, the agricultural eco-efficiency improvement in the eastern region is more significant than that in the central and western regions. (2) The trend of China's agricultural eco-efficiency shifting to a higher level is significant, but the evolution of agricultural eco-efficiency has maintained the stability of the original state, and it is difficult to achieve a leap-forward shift. The geospatial structure plays an important role in the spatial-temporal evolution of agricultural eco-efficiency and the spatial agglomeration is significant. The provinces with higher agricultural eco-efficiency have positive spillover effects, while those with lower agricultural eco-efficiency have negative spillover effects. As a result, the "club convergence" phenomenon of "high agglomeration, low concentration, high radiates low, and low inhibits high" has been gradually formed in the spatial pattern. (3) From the long-term trend prediction, the agricultural eco-efficiency in most provinces gradually shifts upward to a relatively high level, and gradually evolves from a low-to-high incremental pattern. In the context of the low agricultural eco-efficiency, its long-term stable evolution is manifested as a "partial unimodal" distribution; while under the geographical background of higher agricultural eco-efficiency, it has evolved into a "double-peak" distribution of higher-level agglomeration for a long time. Finally, we analyze the shortcomings and what needs to be improved for current research. What's more, we propose that controlling agricultural pollution emissions, inter-regional agro-ecological policy linkages, and strengthening inter-regional agro-ecological cooperation, exchange, and learning can effectively improve China's agricultural eco-efficiency and narrow the gap between provinces.

Key words: agricultural eco-efficiency, spatial-temporal evolution, trend prediction, super efficiency SBM model, spatial Markov chain, China

侯孟阳, 姚顺波. 1978-2016年中国农业生态效率时空演变及趋势预测[J]. 地理学报, 2018, 73(11): 2168-2183.

HOU Mengyang, YAO Shunbo. Spatial-temporal evolution and trend prediction of agricultural eco-efficiency in China: 1978-2016[J]. Acta Geographica Sinica, 2018, 73(11): 2168-2183.

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